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The focus of this work is to develop machine learning systems capable of tissue image analysis in the context of cancer diagnosis and prognosis. Such a system can not only identify new prognostic markers, but can also serve as a standalone clinical prediction rule, the premise being that its non-linear, multivariate nature may be capable of identifying and employing complex patterns that collectively provide accurate cancer diagnosis and prognosis, better than the clinical gold standard. The task, however, is very challenging because of the extremely high resolution of the images, highly heterogeneous microenvironment, multiple sources of noise and artifacts, and low-granularity of ground truth. A starting point of related work which tackles the same task is the extraction of handcrafted features. I investigate the application of machine learning for prognosis using handcrafted features, and develop prognostic machine learning models that demonstrate better performances than baselines based on clinically employed prognostic systems, in two separate cohorts of colorectal and muscle-invasive bladder cancer patients. Moreover, analysis of the proposed methods provides insight behind the prognostic nature of characteristics within the microenvironment, not yet included in the clinical systems. The emergence of deep learning has enabled analysis with images directly. Given the laborious, expensive, and human bias inducing nature of designing and building pipelines for handcrafted feature extraction, I investigate the application of deep learning on tissue images directly. In particular, I propose a framework that allows the training of models …